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Unify Technologies - Senior Engineer / Lead / Architect - Machine Learning / Artificial Intelligence

UNIFY TECHNOLOGIES PVT LTDHyderabad

30+ days ago

Job description

Job Role / Title : Senior / Lead / Architect SDE-ML / AI Engineer

Experience : Senior : 4-15 years

Location : Skill : AI / ML+GenAi+LLM+NLP+Python(Rag, Qualifications :

Minimum 5 years of experience in AI / ML with at least 2+ years in NLP, LLMs, and Generative AI.
Proven expertise in ML architecture design, end-to-end model development, and deployment in production systems.
Strong in Python with deep experience in ML libraries and frameworks such as TensorFlow, PyTorch, Hugging Face, and LangChain.
Sound knowledge of transformer models, embeddings, tokenization, and vector databases (e.g., FAISS, Pinecone).
Experience with cloud-native AI solutions on AWS, Azure, or GCP.
Familiarity with MLOps, model versioning, containerization (Docker), and orchestration tools (e.g., Kubeflow, MLflow).
Hands-on experience in designing and engineering prompts for LLMs to support use cases like summarization, classification, Q&A, and content generation.
Strong understanding of retrieval-augmented generation (RAG) and techniques to combine structured / unstructured data with LLMs.
Excellent problem-solving skills, architectural thinking, and ability to lead complex AI initiatives.
Strong communication, stakeholder management, and technical leadership capabilities.

Key Responsibilities :

Architect and implement end-to-end machine learning and Generative AI solutions for real-world applications.

Design, fine-tune, and deploy models using transformers, embeddings, tokenization, and LLMs for tasks such as summarization, classification, question answering, and content generation.

Develop and maintain high-quality, production-grade ML code in Python, using libraries like TensorFlow, PyTorch, Hugging Face, LangChain, etc.

Build and optimize retrieval-augmented generation (RAG) pipelines by integrating LLMs with structured and unstructured data.

Work with vector databases (e.g., FAISS, Pinecone) to manage semantic search and context retrieval efficiently.

Utilize cloud-native AI services (AWS, GCP, Azure) for model training, deployment, and scaling.

Implement MLOps best practices, including model versioning, containerization (Docker), orchestration (Kubeflow, MLflow), and CI / CD.

(ref : hirist.tech)

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